YOLO Series TensorRT Python/C++

简体中文

Support

YOLOv7、YOLOv6、 YOLOX、 YOLOV5、

Update

• 2022.7.8 support YOLOV7
• 2022.7.3 support TRT int8 post-training quantization

Prepare TRT Env

Python

pip install --upgrade setuptools pip --user
pip install nvidia-pyindex
pip install --upgrade nvidia-tensorrt
pip install pycuda

C++

By Docker

Quick Start

Here is a Python Demo mybe help you quickly understand this repo Link

YOLOv7 [C++, Python Support]

https://github.com/WongKinYiu/yolov7.git

Modify code: Modify the yolo.py as follows: https://github.com/WongKinYiu/yolov7/blob/5f1b78ad614b45c5a98e7afdd295e20033d5ad3c/models/yolo.py#L57

return x if self.training else (torch.cat(z, 1), ) if not self.export else (torch.cat(z, 1), x)

Export onnx

python models/export.py --weights ../yolov7.pt --grid

Convert to TensorRT Engine

python export.py -o onnx-name -e trt-name -p fp32/16/int8

Test

cd yolov7
python trt.py

C++

C++ Demo

YOLOv6 [C++, Python Support]

model	input		FPS	Device	Language
yolov6s	640*640	FP16	360FPS	A100	Python
yolov6s	640*640	FP32	350FPS	A100	Python
yolov6s	640*640	FP32	330FPS	1080Ti	C++
yolov6s	640*640	FP32	300FPS	1080Ti	Python

YOLOv6 bilibili

git clone https://github.com/meituan/YOLOv6.git

Export onnx

python deploy/ONNX/export_onnx.py --weights yolov6s.pt --img 640 --batch 1

Convert to TensorRT Engine

python export.py -o onnx-name -e trt-name -p fp32/16/int8

Test

cd yolov6
python trt.py

C++

C++ Demo

YOLOX [Python Support]

Export ONNX

git clone https://github.com/Megvii-BaseDetection/YOLOX.git

Modify export_onnx.py as model.head.decode_in_inference = True

Modify yolox/models/yolox_head.py

# [batch, n_anchors_all, 85]
# outputs = torch.cat(
#     [x.flatten(start_dim=2) for x in outputs], dim=2

# ).permute(0, 2, 1)
outputs = torch.cat(
    [x.view(-1,int(x.size(1)),int(x.size(2)*x.size(3))) for x in outputs], dim=2

).permute(0, 2, 1)

# outputs[..., :2] = (outputs[..., :2] + grids) * strides
# outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
# return outputs
xy =  (outputs[..., :2] + grids) * strides
wh = torch.exp(outputs[..., 2:4]) * strides
return torch.cat((xy, wh, outputs[..., 4:]), dim=-1)

python3 tools/export_onnx.py --output-name yolox_s.onnx -n yolox-s -c yolox_s.pth

Convert to TensorRT Engine

python export.py -o onnx-name -e trt-name -p fp32/16/int8

Test

cd yolovx
python trt.py

YOLOV5 [Python Support]

Export ONNX

git clone https://github.com/ultralytics/yolov5.git

python path/to/export.py --weights yolov5s.pt --include  onnx 

Convert to TensorRT Engine

python export.py -o onnx-name -e trt-name -p fp32/16/int8

Test

cd yolov5
python trt.py